Breeding of novel crop plants has become increasingly important to
address the challenges of growing earth population, climate change,
and demand of regenerative energy (e.g. bio-fuels) as well as mineral
oil substitutes (bio-polymers).

This requires crop plants with tailored and very specific
properties. Regardless whether these plants are developed by
classical plant breeding or direct biomolecular manipulation
(GM plants), a statistically valid and high-throughput
assessment of the plant's phenotype is always essential.
In order to assess the relevant attributes of a plant's phenotype,
typically the recognition and modelling of spatiotemporal development
patterns needs to be done.

By means of a number of challenging real-world applications this talk
will demonstrate how computational intelligence and machine learning
assists this endeavour.